Signal Enhancement as Minimization of Relevant Information Loss

Provided by: Graz University of Technology
Topic: Mobility
Format: PDF
The authors introduce the notion of relevant information loss for the purpose of casting the signal enhancement problem in information-theoretic terms. They show that many algorithms from machine learning can be reformulated using relevant information loss, which allows their application to the aforementioned problem. As a particular example they analyze principle component analysis for dimensionality reduction, discuss its optimality, and show that the relevant information loss can indeed vanish if the relevant information is concentrated on a lower-dimensional subspace of the input space.

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